Evidence map›Paper›PMID 35634092›Full record

ArticleComputational intelligence and neuroscience2022

Monitoring the Emotional Response to the COVID-19 Pandemic Using Sentiment Analysis: A Case Study in Mexico.

Edgar León-Sandoval, Mahdi Zareei, Liliana Ibeth Barbosa-Santillán, Luis Eduardo Falcón Morales, Antonio Pareja Lora, Gilberto Ochoa Ruiz

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. When Infodemic Meets Epidemic: Systematic Literature Review.JMIR public health and surveillance · 2025
    Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Edgar León-SandovalSchool of Engineering and Sciences, Monterrey Institute of Technology and Higher Education, Monterrey, Mexico.ORCID https://orcid.org/0000-0002-2904-327X
Mahdi ZareeiSchool of Engineering and Sciences, Monterrey Institute of Technology and Higher Education, Monterrey, Mexico.ORCID https://orcid.org/0000-0001-6623-1758
Liliana Ibeth Barbosa-SantillánSchool of Engineering and Sciences, Monterrey Institute of Technology and Higher Education, Monterrey, Mexico.ORCID https://orcid.org/0000-0002-3509-9667
Luis Eduardo Falcón MoralesSchool of Engineering and Sciences, Monterrey Institute of Technology and Higher Education, Monterrey, Mexico.
Antonio Pareja LoraUniversidad de Alcalá, Alcalá, Spain.
Gilberto Ochoa RuizSchool of Engineering and Sciences, Monterrey Institute of Technology and Higher Education, Monterrey, Mexico.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The world is facing the COVID-19 pandemic, leading to an unprecedented change in the lifestyle routines of millions. Beyond the general physical health, financial, and social repercussions of the pandemic, the adopted mitigation measures also present significant challenges in the population's mental health and health programs. It is complex for public organizations to measure the population's mental health in order to incorporate it into their own decision-making process. Traditional survey methods are time-consuming, expensive, and fail to provide the continuous information needed to respond to the rapidly evolving effects of governmental policies on the population's mental health. A significant portion of the population has turned to social media to express the details of their daily life, rendering this public data a rich field for understanding emotional and mental well-being. This study aims to track and measure the sentiment changes of the Mexican population in response to the COVID-19 pandemic. To this end, we analyzed 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies using open sentiment analysis tools. Sentiment analysis polarity scores, which oscillate around -0.15, show a weekly seasonality according to Twitter's usage and a consistently negative outlook from the population. It also remarks on the increased controversy after the governmental decision to terminate the lockdown and the celebrated holidays, which encouraged the people to incur social gatherings. These findings expose the adverse emotional effects of the ongoing pandemic while showing an increase in social media usage rates of 2.38 times, which users employ as a coping mechanism to mitigate the feelings of isolation related to long-term social distancing. The findings have important implications in the mental health infrastructure for ongoing mitigation efforts and feedback on the perception of policies and other measures. The overall trend of the sentiment polarity is 0.0001110643.

Indexed as

COVID-19AttitudeCommunicable Disease ControlEmotionsHumansMexicoPandemics

Identifiers

PMID35634092
PMCPMC9132622

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.